{"_id":"58f5d59402c293230028f0a2","__v":0,"githubsync":"","user":"5767bc73bb15f40e00a28777","version":{"_id":"55faf11ba62ba1170021a9aa","project":"55faf11ba62ba1170021a9a7","__v":40,"createdAt":"2015-09-17T16:58:03.490Z","releaseDate":"2015-09-17T16:58:03.490Z","categories":["55faf11ca62ba1170021a9ab","55faf8f4d0e22017005b8272","55faf91aa62ba1170021a9b5","55faf929a8a7770d00c2c0bd","55faf932a8a7770d00c2c0bf","55faf94b17b9d00d00969f47","55faf958d0e22017005b8274","55faf95fa8a7770d00c2c0c0","55faf96917b9d00d00969f48","55faf970a8a7770d00c2c0c1","55faf98c825d5f19001fa3a6","55faf99aa62ba1170021a9b8","55faf99fa62ba1170021a9b9","55faf9aa17b9d00d00969f49","55faf9b6a8a7770d00c2c0c3","55faf9bda62ba1170021a9ba","5604570090ee490d00440551","5637e8b2fbe1c50d008cb078","5649bb624fa1460d00780add","5671974d1b6b730d008b4823","5671979d60c8e70d006c9760","568e8eef70ca1f0d0035808e","56d0a2081ecc471500f1795e","56d4a0adde40c70b00823ea3","56d96b03dd90610b00270849","56fbb83d8f21c817002af880","573c811bee2b3b2200422be1","576bc92afb62dd20001cda85","5771811e27a5c20e00030dcd","5785191af3a10c0e009b75b0","57bdf84d5d48411900cd8dc0","57ff5c5dc135231700aed806","5804caf792398f0f00e77521","58458b4fba4f1c0f009692bb","586d3c287c6b5b2300c05055","58ef66d88646742f009a0216","58f5d52d7891630f00fe4e77","59a555bccdbd85001bfb1442","5a2a81f688574d001e9934f5","5b080c8d7833b20003ddbb6f"],"is_deprecated":false,"is_hidden":false,"is_beta":true,"is_stable":true,"codename":"","version_clean":"1.0.0","version":"1.0"},"category":{"_id":"58f5d52d7891630f00fe4e77","project":"55faf11ba62ba1170021a9a7","version":"55faf11ba62ba1170021a9aa","__v":0,"sync":{"url":"","isSync":false},"reference":false,"createdAt":"2017-04-18T08:58:21.978Z","from_sync":false,"order":34,"slug":"data-cruncher","title":"DATA CRUNCHER"},"parentDoc":null,"project":"55faf11ba62ba1170021a9a7","updates":[],"next":{"pages":[],"description":""},"createdAt":"2017-04-18T09:00:04.539Z","link_external":false,"link_url":"","sync_unique":"","hidden":false,"api":{"results":{"codes":[]},"settings":"","auth":"required","params":[],"url":""},"isReference":false,"order":7,"body":"At the moment, Data Cruncher offers a set of predefined libraries curated by Seven Bridges bioinformaticians, which are automatically available every time an analysis is started. The libraries are installed using **conda**, as Data Cruncher supports multiple programming languages and **conda** is a language-agnostic package manager. \n\nHere is a list of libraries that are installed by default:\n\n* Python2 \\ Python3:\n    * **path.py, biopython, pymongo, cytoolz, pysam, pyvcf, ipywidgets, beautifulsoup4, sevenbridges-python, cigar, bioservices, intervaltree, appdirs, cssselect, bokeh, scikit-allel, cairo, lxml, cairosvg, rpy2**\n* R:\n    * **r-ggfortify, r, r-stringi, r-pheatmap, r-gplots, bioconductor-ballgown, bioconductor-deseq2, bioconductor-metagenomeseq, bioconductor-biomformat, bioconductor-biocinstaller, sevenbridges-r, r-xml**\n\nThe aforementioned libraries are installed on top of the libraries that are already available in [datascience-notebook](https://github.com/jupyter/docker-stacks/tree/master/datascience-notebook).\n\nYou can also install libraries directly from the notebook and use them during the execution of your analysis. For optimal performance and avoidance of potential conflicts, we recommend using **conda** when installing libraries within your analyses. However, unlike default libraries, libraries installed in that way will not be automatically available next time the analysis is started.","excerpt":"","slug":"about-libraries-in-a-data-cruncher-analysis","type":"basic","title":"About libraries in a Data Cruncher analysis"}

About libraries in a Data Cruncher analysis


At the moment, Data Cruncher offers a set of predefined libraries curated by Seven Bridges bioinformaticians, which are automatically available every time an analysis is started. The libraries are installed using **conda**, as Data Cruncher supports multiple programming languages and **conda** is a language-agnostic package manager. Here is a list of libraries that are installed by default: * Python2 \ Python3: * **path.py, biopython, pymongo, cytoolz, pysam, pyvcf, ipywidgets, beautifulsoup4, sevenbridges-python, cigar, bioservices, intervaltree, appdirs, cssselect, bokeh, scikit-allel, cairo, lxml, cairosvg, rpy2** * R: * **r-ggfortify, r, r-stringi, r-pheatmap, r-gplots, bioconductor-ballgown, bioconductor-deseq2, bioconductor-metagenomeseq, bioconductor-biomformat, bioconductor-biocinstaller, sevenbridges-r, r-xml** The aforementioned libraries are installed on top of the libraries that are already available in [datascience-notebook](https://github.com/jupyter/docker-stacks/tree/master/datascience-notebook). You can also install libraries directly from the notebook and use them during the execution of your analysis. For optimal performance and avoidance of potential conflicts, we recommend using **conda** when installing libraries within your analyses. However, unlike default libraries, libraries installed in that way will not be automatically available next time the analysis is started.